General Overview of Artificial Intelligence for Interstitial Cystitis in Urologyopen access
- Authors
- Cho, Yongwon; Park, Jong Mok; Youn, Seunghyun
- Issue Date
- Nov-2023
- Publisher
- KOREAN CONTINENCE SOC
- Keywords
- Interstitial cystitis; Diagnosis; Treatment; Convolutional neural network; Deep learning; Large language model
- Citation
- International Neurourology Journal, v.27, pp S64 - S72
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- International Neurourology Journal
- Volume
- 27
- Start Page
- S64
- End Page
- S72
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/64951
- DOI
- 10.5213/inj.2346294.147
- ISSN
- 2093-4777
2093-6931
- Abstract
- Our understanding of interstitial cystitis/bladder pain syndrome (IC/BPS) has evolved over time. The diagnosis of IC/BPS is primarily based on symptoms such as urgency, frequency, and bladder or pelvic pain. While the exact causes of IC/BPS remain unclear, it is thought to involve several factors, including abnormalities in the bladder's urothelium, mast cell degranulation within the bladder, inflammation of the bladder, and altered innervation of the bladder. Treatment options include patient education, dietary and lifestyle modifications, medications, intravesical therapy, and surgical interventions. This review article provides insights into IC/BPS, including aspects of treatment, prognosis prediction, and emerging therapeutic options. Additionally, it explores the application of deep learning for diagnosing major diseases associated with IC/BPS.
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- Appears in
Collections - 4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles
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